OpenBandwidth Blog
Notes for teams buying AI throughput, not tokens.
Practical writing on flat-rate inference, heavy developer usage, coding-agent loops, and the operational tradeoffs that show up once AI becomes part of the delivery stack.
Posts
8
Focused articles for heavy AI users.
Themes
6
Focused themes across the current archive.
Audience
Dev
Built for teams shipping with agents every day.
Featured
AI Infrastructure
OpenAI-Compatible Reserved Inference: An Architectural Overview for Production AI Teams
Reserved inference fixes the production runtime underneath the OpenAI-compatible API without forcing teams to rewrite their SDKs, agent frameworks, or tooling.
AI Infrastructure
Agentic Workflow Throughput: How to Measure What Matters in 2026
Production AI agents should be measured by completed loops under realistic concurrency, not by isolated token speed on individual model calls.
Research Workflows
Researchers: Long-Context Evals Without Queue Anxiety
OpenBandwidth gives research teams a flat-rate reserved AI inference layer for long-context eval suites, reruns, and overnight methodology iteration.
Archive
All posts
The current archive is focused on one foundational question: when should teams buy reserved AI throughput instead of living inside token caps?
OpenAI-Compatible Reserved Inference: An Architectural Overview for Production AI Teams
A complete architectural overview of OpenAI-compatible reserved inference in 2026, covering dedicated GPU capacity, fair-share scheduling, prefix-aware KV-cache reuse, and two-line migration.
Agentic Workflow Throughput: How to Measure What Matters in 2026
Tokens per second is the wrong metric for production AI agents. Learn to measure loops per minute, tail latency, and concurrency: the metrics that actually predict AI inference performance in 2026.
Researchers: Long-Context Evals Without Queue Anxiety
A single long-context eval request carries 100,000 tokens or more. Run that across 200 documents and you can exhaust a Tier 1 rate limit before the first result returns.
Cursor and Claude Code Rate Limits in 2026: The Shipping Wall Hidden in Your AI Coding Stack
Cursor and Claude Code rate limits are not a minor annoyance. They are the hidden shipping wall in agentic development, where token metering and shared-pool throttles interrupt real production work.
Reserved AI Bandwidth vs Token Caps: A Pricing Model for Production
Token caps break production AI. Reserved bandwidth is the new pricing model: flat monthly cost, no rate-limit roulette, and OpenAI-compatible access for serious coding workflows.
Predictable Inference Cost: Why AI Unit Economics Break in 2026
Per-token pricing silently breaks AI startup unit economics. Predictable inference cost, flat-rate reserved AI bandwidth, and a unified inference layer restore modelable margins.
AI SaaS Gross Margin in 2026: The Six-Step Model for Predictable Inference Cost
AI SaaS gross margin is collapsing under per-token pricing variance. Use this six-step model to calculate predictable inference cost, fix AI unit economics, and protect AI product gross margins.
Cold-Start Latency in AI Inference: What Aggregator APIs Do Not Tell You About Production AI Speed
AI inference aggregators hide cold-start latency behind p50 benchmarks. Learn what cold-start latency means in production, why bursty AI traffic exposes the gap, and how reserved AI capacity eliminates the tail.